Search

Polycystic ovary syndrome (PCOS) is an endocrinopathy characterized by hyperandrogenism, anovulation, and polycystic ovaries, in which hyperandrogenism manifests by excess androgen and other steroid hormone abnormalities. Mitochondrial fusion is essential in steroidogenesis, while the role of mitochondrial fusion in granulosa cells of hyperandrogenic PCOS patients remains unclear. In this study, mRNA expression of mitochondrial fusion genes mitoguardin1, −2 (MIGA 1, −2) was significantly increased in granulosa cells of hyperandrogenic PCOS but not PCOS with normal androgen levels, their mRNA expression positively correlated with testosterone levels. Dihydrotestosterone (DHT) treatment in mice led to high expression of MIGA2 in granulosa cells of ovulating follicles. Testosterone or forskolin/ phorbol 12-myristate 13-acetate treatments increased expression of MIGA2 and the steroidogenic acute regulatory protein (StAR) in KGN cells. MIGA2 interacted with StAR and induced StAR localization on mitochondria. Furthermore, MIGA2 overexpression significantly increased cAMP-activated protein kinase A (PKA) and phosphorylation of AMP-activated protein kinase (pAMPK) at T172 but inhibited StAR protein expression. However, MIGA2 overexpression increased CYP11A1, HSD3B2, and CYP19A1 mRNA expression. As a result, MIGA2 overexpression decreased progesterone but increased estradiol synthesis. Besides the androgen receptor, testosterone or DHT might also regulate MIGA2 and pAMPK (T172) through LH/choriogonadotropin receptor-mediated PKA signaling. Taken together, these findings indicate that testosterone regulates MIGA2 via PKA/AMP-activated protein kinase signaling in ovarian granulosa cells. It is suggested mitochondrial fusion in ovarian granulosa cells is associated with hyperandrogenism and potentially leads to abnormal steroidogenesis in PCOS.

a new method of random error separation based on wavelet transform and the frequency bands character of measurement data is proposed. Approximates the random error with the detail components of the wavelet decomposition, determines the optimal decomposition level on the frequency intervals between the random errors and other components, and finally gets the random error by reconstructing all the detail components. The method is simply and efficient, avoids the indirect error with modeling and indirect methods, and computer simulation proves the robustness of the separation results.